Same Sex households in the Census

To celebrate Pride month, I was interested in looking into the Census data for our region about the LGBTQ community.

From https://www.census.gov/content/dam/Census/library/publications/2021/acs/acsbr-005.pdf: "As the largest available nationally representative household survey, the American Community Survey (ACS) is useful for examining the characteristics of households and families. Near the beginning of the survey questionnaire, there is a question asking how each household member is related to the householder. The householder is typically someone who owns the home or whose name is on the lease. Understanding each member’s relationship to the householder allows us to distinguish various types of household composition and family structure.

Definitions: Same-sex couple households: These households are identified using the “same-sex spouse” or “same-sex unmarried partner” selections to the relationship to householder question. Total coupled households: The total of those households in which the householder reports having a spouse or unmarried partner in the household. Includes opposite-sex and samesex married and unmarried couples. Unmarried partner: A person in an intimate relationship with the householder, such as a boyfriend or a girlfriend."

Regional and County Totals

I wanted to look into the ACS 5-year (for good sample size) estimates for same-sex married and unmarried couples in the region and by county.

library(tidyverse)
library(sf)
library(leaflet)
library(tidycensus)
library(writexl)
library(htmlwidgets)
source('C:/Users/SChildress/Documents/GitHub/psrc-census-tools/library/psrc_census_config.R')
source('C:/Users/SChildress/Documents/GitHub/psrc-census-tools/library/psrc_census.R')

# See: https://www.census.gov/content/dam/Census/library/publications/2021/acs/acsbr-005.pdf



Sys.getenv("CENSUS_API_KEY")
region_same_sex_married<-psrc_acs_table("B09019_011", "region", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 1 x 6
##   variable  total_region moe_region ACS_Year ACS_Type label                     
##   <chr>            <dbl>      <dbl>    <dbl> <chr>    <chr>                     
## 1 B09019_0~        12259       625.     2019 acs5     Estimate!!Total:!!In hous~
region_same_sex_cohabit<-psrc_acs_table("B09019_013", "region", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 1 x 6
##   variable  total_region moe_region ACS_Year ACS_Type label                     
##   <chr>            <dbl>      <dbl>    <dbl> <chr>    <chr>                     
## 1 B09019_0~         7311       587.     2019 acs5     Estimate!!Total:!!In hous~
# Regional Households

region_hhs<- psrc_acs_table("B08201_001", "region", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 1 x 6
##   variable   total_region moe_region ACS_Year ACS_Type label           
##   <chr>             <dbl>      <dbl>    <dbl> <chr>    <chr>           
## 1 B08201_001      1603060      3393.     2019 acs5     Estimate!!Total:
# I should actually retrieve these values from the table above (lazy)

same_sex_share <-(12259+7311)/1603060

same_sex_share
## [1] 0.0122079
county_same_sex_married<-psrc_acs_table("B09019_011", "county", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 4 x 10
##   GEOID NAME   variable  estimate   moe ACS_Year ACS_Type ACS_Geography label   
##   <chr> <chr>  <chr>        <dbl> <dbl>    <dbl> <chr>    <chr>         <chr>   
## 1 53033 King ~ B09019_0~     7886   486     2019 acs5     county        Estimat~
## 2 53035 Kitsa~ B09019_0~      511   142     2019 acs5     county        Estimat~
## 3 53053 Pierc~ B09019_0~     2053   286     2019 acs5     county        Estimat~
## 4 53061 Snoho~ B09019_0~     1809   230     2019 acs5     county        Estimat~
## # ... with 1 more variable: concept <chr>
county_same_sex_cohabit<-psrc_acs_table("B09019_013", "county", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 4 x 10
##   GEOID NAME   variable estimate   moe ACS_Year ACS_Type ACS_Geography label    
##   <chr> <chr>  <chr>       <dbl> <dbl>    <dbl> <chr>    <chr>         <chr>    
## 1 53033 King ~ B09019_~     5056   509     2019 acs5     county        Estimate~
## 2 53035 Kitsa~ B09019_~      282    93     2019 acs5     county        Estimate~
## 3 53053 Pierc~ B09019_~     1249   234     2019 acs5     county        Estimate~
## 4 53061 Snoho~ B09019_~      724   148     2019 acs5     county        Estimate~
## # ... with 1 more variable: concept <chr>

The data says that there are 12,300 same-sex married couples and 7,300 same-sex unmarried couples. This just seems way too low at around 1.2% of households. There are obviously many same-sex people who are living without partners, but regardless, it makes me question the data.

I retrieved the same sex households by Census Tract to see what the geographic results look like. Many tracts are within the MOE.

Same Sex Couples by Census Tract

tract_same_sex_married<-psrc_acs_table("B09019_011", "tract", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 776 x 10
##    GEOID  NAME   variable estimate   moe ACS_Year ACS_Type ACS_Geography label  
##    <chr>  <chr>  <chr>       <dbl> <dbl>    <dbl> <chr>    <chr>         <chr>  
##  1 53033~ Censu~ B09019_~       30    34     2019 acs5     tract         Estima~
##  2 53033~ Censu~ B09019_~       36    40     2019 acs5     tract         Estima~
##  3 53033~ Censu~ B09019_~       12    19     2019 acs5     tract         Estima~
##  4 53033~ Censu~ B09019_~       19    31     2019 acs5     tract         Estima~
##  5 53033~ Censu~ B09019_~       29    34     2019 acs5     tract         Estima~
##  6 53033~ Censu~ B09019_~       13    19     2019 acs5     tract         Estima~
##  7 53033~ Censu~ B09019_~        0    17     2019 acs5     tract         Estima~
##  8 53033~ Censu~ B09019_~       46    44     2019 acs5     tract         Estima~
##  9 53033~ Censu~ B09019_~       13    20     2019 acs5     tract         Estima~
## 10 53033~ Censu~ B09019_~        0    12     2019 acs5     tract         Estima~
## # ... with 766 more rows, and 1 more variable: concept <chr>
tract_same_sex_cohabit<-psrc_acs_table("B09019_013", "tract", 2019,'acs5')
## Getting data from the 2015-2019 5-year ACS
## # A tibble: 776 x 10
##    GEOID  NAME   variable estimate   moe ACS_Year ACS_Type ACS_Geography label  
##    <chr>  <chr>  <chr>       <dbl> <dbl>    <dbl> <chr>    <chr>         <chr>  
##  1 53033~ Censu~ B09019_~       11    19     2019 acs5     tract         Estima~
##  2 53033~ Censu~ B09019_~       19    32     2019 acs5     tract         Estima~
##  3 53033~ Censu~ B09019_~       22    24     2019 acs5     tract         Estima~
##  4 53033~ Censu~ B09019_~        0    17     2019 acs5     tract         Estima~
##  5 53033~ Censu~ B09019_~        0    17     2019 acs5     tract         Estima~
##  6 53033~ Censu~ B09019_~        0    12     2019 acs5     tract         Estima~
##  7 53033~ Censu~ B09019_~       25    29     2019 acs5     tract         Estima~
##  8 53033~ Censu~ B09019_~       40    40     2019 acs5     tract         Estima~
##  9 53033~ Censu~ B09019_~       34    29     2019 acs5     tract         Estima~
## 10 53033~ Censu~ B09019_~       13    14     2019 acs5     tract         Estima~
## # ... with 766 more rows, and 1 more variable: concept <chr>
ss_married_map<-create_tract_map(tract_same_sex_married)
## Reading layer `dbo.tract2010_nowater' from data source `MSSQL:server=AWS-PROD-SQL\Sockeye;database=ElmerGeo;trusted_connection=yes' using driver `MSSQLSpatial'
## Simple feature collection with 773 features and 19 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 1099353 ymin: -97548.53 xmax: 1622631 ymax: 477101.5
## projected CRS:  NAD83 / Washington North (ftUS)
ss_cohabit_map<-create_tract_map(tract_same_sex_cohabit)
## Reading layer `dbo.tract2010_nowater' from data source `MSSQL:server=AWS-PROD-SQL\Sockeye;database=ElmerGeo;trusted_connection=yes' using driver `MSSQLSpatial'
## Simple feature collection with 773 features and 19 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 1099353 ymin: -97548.53 xmax: 1622631 ymax: 477101.5
## projected CRS:  NAD83 / Washington North (ftUS)
ss_married_map
ss_cohabit_map

I guess the maps look reasonable?